A Similarity Measure for the Description Logic EL with Unfoldable Terminologies

2013 ◽  
Author(s):  
Boontawee Suntisrivaraporn
Keyword(s):  
Similarity Measure ◽  
Description Logic

Personalizing a Concept Similarity Measure in the Description Logic DLELH with Preference Profile

2018 ◽  
Vol 37 (3) ◽  
pp. 581-613 ◽  
Author(s):  
Teeradaj Racharak ◽  
Boontawee Suntisrivaraporn ◽  
Satoshi Tojo
Keyword(s):  
Similarity Measure ◽  
Description Logic ◽  
Preference Profile ◽  
Concept Similarity

scholarly journals Toward semantic similarity measure between concepts in an ontology

2019 ◽  
Vol 14 (3) ◽  
pp. 1356
Author(s):  
Suwan Tongphu
Keyword(s):  
Semantic Similarity ◽  
Similarity Measure ◽  
Description Logic ◽  
Classical Problem ◽  
Similarity Score ◽  
New Method ◽  
Snomed Ct ◽  
Major Drawback ◽  
Semantic Similarity Measure ◽  
Concept Definition

<p>A similarity measure is one classical problem in Description Logic which aims at identifying the similarity between concepts in an ontology. Finding a hierarchy distance among concepts in an ontology is one popular technique. However, one major drawback of such a technique is that it usually ignores a concept definition analysis. This work introduces a new method for similarity measure. The proposed system semantically analyzes structures of two concept descriptions and then computes the similarity score based on the number of shared features. The efficiency of the proposed algorithm is measured by means of the satisfaction of desirable properties and intensive experiments on the Snomed ct ontology.</p>

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

The Extended-Average Common Submatrix Similarity Measure with Application to Handwritten Character Images

Informatica ◽  
2018 ◽  
Vol 29 (3) ◽  
pp. 399-420
Author(s):  
Alessia Amelio ◽  
Darko Brodić ◽  
Radmila Janković
Keyword(s):  
Similarity Measure ◽  
Handwritten Character
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